All resources
Modern businesses generate so much data every single day. And still they struggle to grow as fast as the amount of data they collect. Data analytics is often hailed as the secret weapon for business growth - but why do so many companies struggle to make sense of their data?
From scattered reports with conflicting numbers, businesses today face common, yet critical challenges that can make even the most data-rich companies feel lost. But what if there was a way to turn this chaos into clarity?
In this article, we'll explore the 5 core challenges that most businesses face when it comes to data analytics. You'll also learn about a proven 12-step framework to overcome these problems and set your business up for success.
By the end of this article, you'll have a solid understanding of the typical issues businesses encounter with data and the tools needed to solve them. Ready? Let's dive in.
Why are so many businesses struggling with data analytics?
A Gartner study shows that 54% of marketers are dissatisfied with their analytics. I bet this isn’t limited to marketing departments alone; it’s spread across the whole business.
💡Check out this video on the top 5 challenges every business faces in data analytics and how to solve them, or read a more detailed article below.
With this being said, let’s dive into the 5 most critical problems every business faces when dealing with data.
The data analytics landscape is constantly evolving, and that makes it even more challenging for businesses to address these problems effectively.
First up, there is no clear analytics roadmap.
One of the biggest challenges businesses face with data analytics is that there’s no clear analytics roadmap, no structured plan for any data analytics project. They often have no idea what they should be doing when measuring and optimizing their business processes.
Imagine diving into a new video game without knowing the rules, objectives, or how to achieve your goals – sounds tricky, right? That’s exactly how many businesses feel when dealing with data. They’re overwhelmed by a flood of information, from website interactions to detailed sales metrics, but without a solid plan, it’s just a sea of numbers and charts. Without clear objectives, it becomes difficult to effectively analyze data and generate actionable insights.
Without that roadmap, businesses may chase the latest buzzword or trend, only to realize later that it wasn’t what they needed. Data is massive, and it’s easy to get lost in it. But once you have a proper plan, things start to make sense, and you can level up your business game.
Another significant issue is data silos. It’s like each piece of data is in its own little world, making it hard to trust what you’re working with.
Companies collect tons of information - sales figures here, website visits there, and maybe some social media stats thrown in. Integrating multiple data sources from different departments or platforms is a major challenge, often resulting in data silos. Without a data analyst to bring it all together, it becomes nearly impossible to handle all that data effectively.
💡Watch this video to explore the role of a data analyst, their core responsibilities, and the essential skills needed to thrive in the analytics field. It’s ideal for beginners ready to launch their career in data!
The third problem is trust issues with the data.
If business users aren’t confident in the accuracy of their data or even where it comes from, it’s like building a house on quicksand. Inaccurate data can erode trust and lead to poor decision-making. Verifying historical data is crucial to ensure its reliability for current analysis. Without trust, making bold decisions becomes impossible.
When your data is scattered across different tools or departments, it’s hard to see the full picture. Without that clear view, it’s no surprise that businesses get stuck, trying to make sense of reports that don’t add up. And when there’s no trust in the data, teams start second-guessing everything, unsure whether they’re making the right calls.
Inaccurate, incomplete, or inconsistent data can lead to misleading conclusions and poor decision-making. These issues often stem from sloppy data collection, insufficient validation, or skipping essential data cleaning steps. If your data isn’t reliable, your insights won’t be either.
At the same time, data security plays a critical role in trust. If your data isn’t properly managed or protected, it could fall into the wrong hands, or lead to mistakes you didn’t see coming. Without clear rules and access controls in place, businesses risk breaking privacy laws or making decisions based on flawed or biased data. To truly trust your analytics, you need both clean data and strong security behind it.
Even when businesses have a data analyst and some data, scattered reports remain a significant issue.
Imagine this: you’ve got a sales report from your website analytics tool and another from your sales team: Both are reporting on the same thing, but why do the numbers look so different? When multiple, uncoordinated data tools are used, reports often become scattered and inconsistent, making it difficult to get a unified view.
Why does Google Analytics show 900 sales, but your CRM or CMS says it’s 543?…
And then QuickBooks shows only 487… Relying on manual processes to compile and reconcile data from these various tools is inefficient and prone to errors.
Confusing, right? That’s exactly what it feels like when dealing with unblended data- data that isn’t speaking to each other. It’s not enough to just have the data. If it’s scattered, inconsistent, or just plain messy, it’s as good as useless. Scattered reports often lead to poorly designed dashboards, creating data visualization challenges that confuse rather than clarify insights.
The final problem is that, without proper systems, businesses often make random decisions instead of informed, data-driven strategic decisions that can guide long-term success.
You know that feeling when you’re just throwing ideas at the wall, hoping something sticks? That’s where many businesses end up when making decisions.
Why is our conversion rate down this month? No one’s sure, so maybe the homepage banner should be changed!
But wait, should we focus on redoing the email campaign instead?
Or maybe it’s time to host a webinar?
And the cycle continues again and again with no clear direction. Meanwhile, the business is getting nowhere fast.
The saddest part? You’re sitting on a goldmine of data that could guide every decision, but instead, it’s like navigating through fog without a GPS. Instead of leveraging data insights to optimize processes and operations, many are just guessing. By using data analytics, businesses can achieve better decision-making and drive improved outcomes across all areas.
To move beyond common challenges in analytics, businesses need to adopt strategic best practices that support long-term, scalable data use. These approaches help ensure your data is not only accurate and accessible but also secure, compliant, and actionable.
Organizations increasingly rely on cloud platforms to manage data workflows. Whether used exclusively or as part of a hybrid setup, cloud solutions reduce the need for on-site infrastructure and offer benefits like high-speed processing, automated backups, advanced security, and the ability to scale with growing analytics needs.
Reliable insights start with high-quality data. Investing in automated data quality tools can help ensure datasets are clean, complete, and ready for analysis. Many solutions now use AI and machine learning to streamline data preparation, flagging errors, filling gaps, and standardizing inconsistent inputs to reduce manual effort and increase trust in your reports.
A well-defined data governance framework outlines how your organization collects, labels, stores, and protects data. It also improves internal accessibility by reducing silos and ensuring consistent terminology. Beyond internal clarity, governance also supports compliance with privacy regulations, helping your team meet legal requirements and reduce operational risk.
With regulations like GDPR and HIPAA raising the stakes, protecting sensitive data is non-negotiable. Best practices include using role-based access controls, encrypting personal information, obtaining user consent where applicable, and being transparent about how data is used. Privacy-first analytics builds customer trust and reduces the risk of breaches or fines.
Data analysts and scientists are central to upholding these best practices. They should be equipped with the knowledge of current regulations, the ability to spot and correct data quality issues, and an awareness of potential biases in models or metrics. Embedding ethics and compliance into daily workflows ensures your analytics function supports both growth and accountability.
When resources are tight, it’s essential to focus on analytics projects that drive the most value. Start by prioritizing initiatives that align with core business goals. Leverage advanced analytics tools, like machine learning and cloud computing, to help your team analyze large volumes of data from multiple sources efficiently.
Even with smaller teams, data analysts and data scientists can use these tools to identify trends, uncover hidden patterns, and generate insights that enhance the customer experience and support long-term business growth.
So, we've discussed the common challenges businesses face in the world of data analytics.
It can feel like you're lost in a dense forest, right?
But here's the good news: there's a way out – a clear path that leads straight to business success, backed and driven by data.
I'm about to flip your understanding of building a solid analytics system. Look, you're reading this because you probably feel that pain – maybe not all five challenges, but at least some of them.
I want you to know it can be different. You know there's a missing piece in your data puzzle. Bridging the gap from “we've got potential” to “we're crushing it using data” comes down to three game-changers.
Let's break it down into 3 pillars of success:
The first step is YOU.
Your commitment and drive, your passion, are what will push your business forward, no matter your role. To effectively contribute to data analytics initiatives, it is essential to develop the necessary skills that are in high demand for analytics roles. Whether you’re a CEO or a data analyst, or a finance manager, you can start heading in the right direction. Having qualified professionals on your team is also crucial for tackling complex data challenges and ensuring analytics success.
Next, you need a clear roadmap. Without a plan, it's like sailing without a compass.
Our 12-step roadmap will take you from confusion to clarity, helping you confidently navigate your data strategy.
Finally, you need the right tools. Whether it’s data storage, visualization tools, or analytics platforms, having the right tech stack is critical for success. The emergence of new tools and technologies in data analytics has expanded capabilities, improved efficiency, and introduced new opportunities for advanced analysis.
Big data technologies are essential for managing and analyzing large volumes of data, enhancing interoperability, and supporting advanced analytics like machine learning. Tools like Power BI enable users across departments to create interactive dashboards, democratizing analytics and supporting data-driven decision-making.
💡Explore the top Digital analytics solutions in this article and discover how to make data-driven decisions that boost your business growth. Perfect for marketers aiming to optimize their strategies with the best tools!
After working with over 165,000 users worldwide and understanding their analytics pain points, we developed a 12-step Roadmap to Data Analytics Mastery.
This roadmap took us 8 years to perfect, and now it’s easy to follow and implement. It provides a structured approach for turning raw data into actionable insights, guiding you through each essential step.
Let’s walk through it:
The roadmap is built around 4 key stages: Plan, Collect, Prepare, Deliver.
Here’s a quick overview:
The first stage, Plan, is all about setting out with clarity. It's about knowing exactly where you're headed as a business.
In this stage, we focus on collecting the right pieces of information to calculate the metrics and dimensions we’ve defined, ensuring that our data is complete, trusted, and fits our puzzle perfectly. Gathering massive amounts of data from various sources can be challenging, but it is essential for robust analysis. It is also important to collect different data types, such as structured, unstructured, and semi-structured data, to ensure a comprehensive and accurate understanding.
It’s about making sure every piece of data aligns with the plan we’ve set so we can move forward with confidence that our information is accurate and actionable.
Then comes the Prepare or Transform stage. It’s not enough to just have data; we need to clean it, merge it, and slice and dice it. Efficiently preparing data is crucial, as streamlined and automated data preparation can significantly reduce the time and effort required for analysis. We have to connect the dots, make the data communicate with each other, and ensure it works for us.
However, preparing data can be particularly challenging when dealing with legacy systems that may not integrate well with modern analytics platforms. This stage is all about transforming raw data into something meaningful, so it can provide accurate insights and drive informed decisions.
Finally, Deliver. This is where everything comes together, ensuring that the insights we’ve gathered are not only accessible but also actionable. It’s about turning those report insights into tangible business growth. By leveraging predictive analytics, organizations can provide forward-looking insights that drive business growth and improve decision-making.
Machine learning algorithms play a key role in automating the generation and delivery of these actionable insights, making the process more efficient and scalable. Too many competing analytics systems and tools lead to waste and unnecessary complexity within organizations.
Now that you’ve got the basics, I believe you have the foundation and structure for your analytics projects in place - the one that supports all your analytics and decision-making processes.
Data can seem overwhelming, but with the right roadmap and tools, it can become your most powerful asset for growth.
Don’t let scattered reports or data silos hold you back from unlocking the full potential of your business. With the right tools and advanced techniques like natural language processing, you can extract valuable insights from unstructured data and boost your analytics capabilities.
By establishing a structured approach, breaking down silos, and ensuring your data is aligned and actionable, you’re not just managing data – you’re driving smarter decisions that lead to sustainable growth. Data protection and data security are essential for safeguarding sensitive information, ensuring privacy, and maintaining trust as you leverage analytics.
Remember, data isn’t just about numbers; it’s about the insights that fuel progress.
With the right system, you can shift from reactive decision-making to proactive, data-driven strategies that propel your business forward. Whether it’s optimizing supply chains or streamlining internal workflows, analytics can improve efficiency and reduce costs across the board.
